Vehicle Detection¶
The goals / steps of this project are the following:
- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
- Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
- Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
- Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
- Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles. Estimate a bounding box for vehicles detected.
1. Exploring data¶
In [29]:
# read the training data and display some statistics
import os
import glob
# get the file names of vehicle images
basedir = '../p5/vehicles/'
image_types = os.listdir(basedir)
cars = []
for imtype in image_types:
cars.extend(glob.glob(basedir + imtype + '/*'))
print('Number of vehicle images found: ', len(cars))
# get the file names of non-vehicle images
basedir = '../p5/non-vehicles/'
image_types = os.listdir(basedir)
notcars = []
for imtype in image_types:
notcars.extend(glob.glob(basedir + imtype + '/*'))
print('Number of non-vehicle images found: ', len(notcars))
# display a random car image and not car image
# Choose random car /not-car indices
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
# Read in car / not car images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
f.tight_layout()
ax1.imshow(car_image)
ax1.set_title('Car', fontsize=20)
ax2.imshow(notcar_image)
ax2.set_title('Not Car', fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [30]:
# imports
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import time
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
from collections import deque
2. Histogram of Oriented Gradients (HOG)¶
2.1 Fuctions defined in lesson¶
Below is a list of functions that are mostly straight copies of the functions defined in the course material
In [31]:
### Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute color histogram features
# Pass the color_space flag as 3-letter all caps string
# like 'HSV' or 'LUV' etc.
def bin_spatial(img, color_space='RGB', size=(32, 32)):
# Convert image to new color space (if specified)
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(feature_image, size).ravel()
# Return the feature vector
return features
# Define a function to compute color histogram features
def color_hist(img, nbins=32):
# Compute the histogram of the RGB channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins)
channel2_hist = np.histogram(img[:,:,1], bins=nbins)
channel3_hist = np.histogram(img[:,:,2], bins=nbins)
# Generating bin centers
# bin_edges = channel1_hist[1]
# bin_centers = (bin_edges[1:] + bin_edges[0:len(bin_edges)-1])/2
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the feature vector
return hist_features
# Define a function to extract features from a list of images
def extract_features(imgs, cspace='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9, hist_range=(0, 256),
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if cspace != 'RGB':
if cspace == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
# Apply bin_spatial() to get spatial color features
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
# Apply color_hist() also with a color space option now
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
file_features.append(hog_features)
# Append the new feature vector to the features list
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
draw_img = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(draw_img, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return draw_img
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True, vis=False):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
hog_image = None
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
if vis == True:
hog_features, hog_image = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=True, feature_vec=True)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
if vis == True:
return np.concatenate(img_features), hog_image
else:
return np.concatenate(img_features)
# Define a function you will pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
# Define a function for plotting multiple images
def visualize(fig, rows, cols, imgs, titles):
for i, img in enumerate(imgs):
plt.subplot(rows, cols, i+1)
plt.title(i+1)
img_dims = len(img.shape)
if img_dims < 3:
plt.imshow(img, cmap='hot')
plt.title(titles[i])
else:
plt.imshow(img)
plt.title(titles[i])
2.2 Visualize the HOG of a car image and a not car image¶
In [32]:
%matplotlib inline
# Choose random car /not-car indices
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
# Read in car / not car images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
# define feature parameters
color_space = 'YCrCb' # can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 12
pix_per_cell = 8
cell_per_block = 2
hog_channel = 0 # Can be 0, 1, 2 or "ALL"
spatial_size = (32, 32)
hist_bins = 32 # number of histogram bins
spatial_feat = True
hist_feat = True
hog_feat = True
car_features, car_hog_image = single_img_features(car_image, color_space=color_space, spatial_size=spatial_size,
hist_bins=hist_bins, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
notcar_features, notcar_hog_image = single_img_features(notcar_image, color_space=color_space, spatial_size=spatial_size,
hist_bins=hist_bins, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car HOG image', 'notcar image', 'notcar HOG image']
fig = plt.figure(figsize=(12,3))
visualize(fig, 1, 4, images, titles)
2.3 Train the classifier¶
In [39]:
# Define feature parameters
color_space = 'YCrCb' # can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9
pix_per_cell = 8
cell_per_block = 2
hog_channel = 'ALL' # 0, 1, 2 or 'ALL'
spatial_size = (32, 32)
hist_bins = 32
spatial_feat = True
hist_feat = True
hog_feat = True
t=time.time()
n_samples = 1000
random_idxs = np.random.randint(0, len(cars), n_samples)
test_cars = cars #np.array(cars)[random_idxs]
test_notcars = notcars #np.array(notcars)[random_idxs]
car_features = extract_features(test_cars, cspace=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(test_notcars, cspace=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
print(time.time()-t, 'Seconds to computer features...')
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized traiing and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.1, random_state=rand_state)
print('Using:', orient, 'orientations,', pix_per_cell, 'pixels per cell,', cell_per_block, 'cells per block,',
hist_bins, 'histogram bins, and', spatial_size, 'spatial sampling')
print('Feature vector length:', len(X_train[0]))
# USe a linear SVC
svc = LinearSVC()
# Check the training time for SVC
t=time.time()
svc.fit(X_train, y_train)
print(round(time.time()-t, 2), 'seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
3. Sliding Window Search¶
3.1 Use the classifier and sliding window to search for cars in the test images¶
In [54]:
searchpath = 'test_images/*'
example_images = glob.glob(searchpath)
images = []
titles = []
y_start_stop = [400, 656] # Min and Max in y to search in slide_window()
overlap = 0.5
for img_src in example_images:
t1 = time.time()
img = mpimg.imread(img_src)
draw_img = np.copy(img)
img = img.astype(np.float32)/255
print(np.min(img), np.max(img))
windows = slide_window(img, x_start_stop=[None, None], y_start_stop=y_start_stop,
xy_window=(96,96), xy_overlap=(overlap, overlap))
hot_windows = search_windows(img, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
window_img = draw_boxes(draw_img, hot_windows, color=(0, 0, 255), thick=6)
images.append(window_img)
titles.append('')
print(time.time()-t, 'seconds to process one image searching', len(windows), 'windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 5, 2, images, titles)
In [41]:
def convert_color(img, conv='RGB2YCrCb'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'BGR2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
if conv == 'RGB2LUV':
return ccv2.cvtColor(img, cv2.COLOR_RGB2LUV)
3.2 Heatmap and threshold functions¶
In [42]:
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
3.3 Define a single function that can extract features using hog sub-sampling and make predictions¶
In [43]:
def find_cars(img, ystart, ystop, scale, svc, X_scaler):
orient = 9
pix_per_cell = 8
cell_per_block = 2
hog_channel = 'ALL' # 0, 1, 2 or 'ALL'
spatial_size = (32, 32)
hist_bins = 32
img_boxes = []
t = time.time()
count = 0
draw_img = np.copy(img)
img = img.astype(np.float32)/255
# make a heatmap of zeros
heatmap = np.zeros_like(img[:,:,0])
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
count += 1
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
img_boxes.append(((xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart)))
heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1
return draw_img, img_boxes, heatmap, count
3.4 Try the pipeline on test images¶
In [50]:
out_images = []
out_maps = []
out_titles = []
ystart = 400
ystop = 656
scale = 1.5
# iterate over test images
for img_src in example_images:
img = mpimg.imread(img_src)
out_img, bboxes, heatmap, count = find_cars(img, ystart, ystop, scale, svc, X_scaler)
# heatmap = apply_threshold(heatmap, 1)
labels = label(heatmap)
# draw bounding boxes on a copy of the image
draw_img = draw_labeled_bboxes(np.copy(img), labels)
out_images.append(draw_img)
out_titles.append(img_src[-9:])
out_images.append(heatmap)
out_titles.append(img_src[-9:])
out_maps.append(heatmap)
fig = plt.figure(figsize=(12,28), dpi=300)
visualize(fig, 8, 2, out_images, out_titles)
4. Video Implementation¶
I tried search at different scales. But it doesn't seem to give me noticible improvements so I chose to leave it out and search at 1.5 scale only.
In [51]:
ystart = 400
ystop = 656
heatmaps = deque(maxlen=10)
def process_image(img):
heat = np.zeros_like(img[:,:,0]).astype(np.float)
# search at origin scale first
# ystart = 350
# ystop = 500
# scale = 1
# out_img, bboxes, heatmap, count = find_cars(img, ystart, ystop, scale, svc, X_scaler)
# current_heatmap = add_heat(heat, bboxes)
ystart = 400
ystop = 656
scale = 1.5 # search at origin scale first
out_img, bboxes, heatmap, count = find_cars(img, ystart, ystop, scale, svc, X_scaler)
current_heatmap = add_heat(heat, bboxes)
# ystart = 400
# ystop = 656
# scale = 2 # search at origin scale first
# out_img, bboxes, heatmap, count = find_cars(img, ystart, ystop, scale, svc, X_scaler)
# current_heatmap = add_heat(heat, bboxes)
heatmaps.append(current_heatmap)
heatmap_sum = sum(heatmaps)
heatmap = apply_threshold(heatmap_sum, 8)
labels = label(heatmap)
# draw bounding boxes on a copy of the image
draw_img = draw_labeled_bboxes(np.copy(img), labels)
return draw_img
In [52]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
output_file = 'output_videos/project_video.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
# clip = VideoFileClip("project_video.mp4").subclip(25,50)
clip = VideoFileClip("project_video.mp4")
write_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time write_clip.write_videofile(output_file, audio=False)
In [53]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output_file))
Out[53]:
NOTE: This is not used.
In [48]:
# Define a class to receive the characteristics of each vehicle detection
# Objects defined as "Vehicles" will be where multiple overlapping detections exist in the heatmap
class Vehicle():
def __init__(self):
sefl.detected = False # was the Vehicle detected in the last iteration
self.n_detections = 0 # Number of times this vehicle has been detected
self.n_nondetections = 0 # Number of consecutive times this car has not been detected since detection
self.xpixels = None # Pixel x values of last detection
self.ypixels = None # Pixel y values of last detection
self.recent_xfitted = [] # x position of the last n fits of the bounding box
self.bestx = None # average x position of the last n fits
self.recent_yfitted = [] # y position of the last n fits of the bounding box
self.besty = None # average y position of the last n fits
self.recent_wfitted = [] # width of the last n fits of the bounding box
self.bestw = None # average width of the last n fits
self.recent_hfitted = [] # height of the last n fits of the bounding box
self.besth = None # average height of the last n fits